Since [1]} uses Jigsaw solver in domain generalization setting. Here, we study using Jigsaw instead of RotNet in our SSDA setting. We tried \(\lambda _{ssl} \in \lbrace 0.7, 1.0\rbrace \)  and \(\lambda _{ent} \in \lbrace 0.01, 0.1\rbrace \)  for real to sketch pair and picked the best combination. These are the parameters used in [1]}. We do not do knowledge distillation for these experiments. We list our results in Table REF . We found that Jigsaw results are close to the S+T baseline. Note that Jigsaw is shown to be more effective than RotNet in [1]}, but it is significantly worse than RotNet in our SSDA setting. We empirically conclude that Jigsaw does not generalize well in the case of single source domain.
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